Unmasked: A Narrative Exploration of Identity in Female Educators with ADHD
Bibliographic record
Abstract
This qualitative narrative inquiry explores the lived experiences of five adult female educators in Ontario, Canada who were diagnosed with or self-identify as having Attention Deficit Hyperactivity Disorder (ADHD). Participants teach across elementary, secondary, and post-secondary levels. A college consultant in educational development was also interviewed as a key informant to provide additional context. Data was collected through one semi-structured interview per subject and validated through member-checking and a collaborative reflection process in which participants could help co-author their narrative vignettes. The study investigates how ADHD intersects with gender, professional identity, pedagogy, and institutional systems, especially in women, most of whom discovered their ADHD in adulthood. While each story is unique, the participants from this study described bringing fun, sensitivity, creativity and empathy to a system which has traditionally pathologized ADHD rather than recognize it as an exceptional difference. Themes included paradoxes of identity formation, systemic expectations and pedagogical values. Across educational contexts (elementary, secondary, and post-secondary) systems were seen as underfunded, outdated and unsustainable. Participants shared experiences of burnout and chronic pain as well as some struggles navigating ableism, and internalized stigma. They also reflected on their past as young students with unnamed ADHD and how these experiences shaped their relationships and sense of self. Many participants described teaching in ways they wish they had been taught and shared some examples of how they connect with struggling learners. Common challenges and/or traits included: all-or-nothing thinking, administrative overwhelm, emotional dysregulation, and struggling in environments that are not suited for their physical and mental well-being. This study acknowledges the importance of expansive supportive and inclusionary measures. A student’s learning environment is a teacher’s work environment; therefore, where empathy and support helps one group, it will likely benefit others. The study calls for the creation and implementation of flexible, human-centered approaches to education that acknowledge and value diverse cognitive and emotional experiences within our schools as learning environments and as workplaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".